Retail & Hospitality /Decisioning live field guide · 9 min

Demand-Matched Roster Builder

A draft weekly roster matched to your forecast demand and held inside the labour budget, with every shift checked against the Restaurant Award and anything risky flagged for the manager to fix before sign-off.

Open the live demo

theater/demos/retail-hospo_demand-matched-roster-builder.html · sandbox · read-only

Open

FIG. 1

The live demo, running on fabricated data. Open it to step through the full flow — every output is shown for a person to approve before anything happens.

How it would work

Reads your POS covers, bookings and trading patterns to draft a roster against forecast demand, runs a deterministic Restaurant Award check on every shift, and hands the manager a roster to adjust and sign off before anything reaches staff.

Input 01

Covers, roster, staff, budget

POS sales and covers by time of day, current bookings and a draft roster, staff availability and ages, and the labour budget — plus weather and known local events that move demand.

Agent 02

Forecasts, matches, checks

Forecasts covers per day-part from roughly four weeks of trading, converts that to a needed headcount, compares it to the rostered cover, and runs each shift against the Restaurant Award MA000119 rules.

Output 03

A draft, flagged for sign-off

A demand-matched draft with understaffed peaks, over-rostered lulls and award flags called out line by line, for the manager to adjust and approve. Nothing publishes to staff until they sign off.

Where it works well

It makes the day-part mismatch visible — the gap a single headcount-for-the-day view always hides.

A lunch peak short three staff costs sales and waits; a 3–5pm lull carrying three surplus people costs wages on Saturday penalty rates — both on the same day.
Best for a multi-venue café, restaurant or QSR group where a manager builds rosters weekly and the POS records covers by time of day.
The evening a good manager loses to the spreadsheet comes back to the floor — the roster conversation becomes "here's where the forecast disagrees with your draft."

The slow, invisible problem is the gap between the roster a venue runs and the demand it serves. Labour is typically among the largest cost lines on thin hospitality margins, yet most rosters are rebuilt each week from last year's pattern — and the mismatch is rarely a whole day, it's a day-part.

Where it works badly

It is confidently wrong when the demand signal is thin or the day is genuinely unusual.

A nearby event, a road closure or a 40-degree day that empties or fills the room is invisible to it unless it reaches a system the model can read.
Not worth it for a single small site with a stable, mostly-permanent team rostered the same way every week — there is no demand curve to chase.
The demo badges POS-only days differently from days enriched with bookings and weather, precisely so a thin forecast isn't trusted like a rich one.

The honest test

Pull last month's covers-by-hour against last month's rostered hours for one venue. If they already track each other closely, you don't have the problem this solves.

If phone bookings and walk-ins never reach the POS, the model under-forecasts your real peaks and will suggest trimming a shift that turns out busy. A one-off it has never seen sits outside the four-week pattern it learns from — and that day's forecast is a guess dressed as a number.

What it doesn't do — and shouldn't

It drafts and checks. The manager decides who works and signs off.

WHAT IT DOES

Surfaces the forecast-versus-coverage gap per day-part and suggests where to move hours

Flags shifts that may trip an award rule — minimum engagement, the under-18 shift cap, an overtime threshold

Cites the Restaurant Award clause behind each flag and stops there

WHAT IT WON’T

Publish the roster to staff

Decide who works a hard Saturday, who's training up, or who keeps their day off

Certify a shift as award-compliant

A roster carries obligations under the Restaurant Industry Award MA000119, and getting penalty rates, junior limits or minimum engagement wrong is a wage-underpayment exposure enforced by the Fair Work Ombudsman, not a rounding error. Awards get interpreted, enterprise agreements override them, and the rules change at the annual wage review — so the tool flags and a person confirms.

What your data has to look like

Covers by time of day, recent enough to reflect how you trade now, plus the staff fields the award turns on.

Typical readiness

across orgs we see, before the first job

POS that records covers or sales by time of day

Usual weak point

Recent trading history that reflects current trade

Needs shaping

Bookings and known events in a readable system

Needs shaping

Staff employment type, age and exact shift times

Usual weak point

The current Restaurant Award MA000119 rule set

Usually ready

The real first job

Most venues have covers-by-hour but ages missing from the rostering system, or a POS feed that only syncs weekly. Fixing how that information is captured and kept current — a matter of capture and integration, not a new tool — is usually the real first job, and it's bigger and more valuable than the forecasting layer on top.

Right fit if…

You build rosters weekly across multiple venues against a labour budget

Your POS records covers by time of day, not just a daily total

Saturday and evening trade swings enough that day-part mismatch costs you real money

A manager currently loses an evening a week rebuilding the grid from last year's pattern

Walk away if…

A single small site with a stable, mostly-permanent team rostered the same way each week

Bookings and walk-ins never reach a system the model can read

Your POS only gives an end-of-day total, with no covers by time of day

You want a tool that signs off award compliance for you

Open questions

The worried-buyer questions, answered straight

It can — a forecast is a forecast, and a one-off it has never seen (a road closure, a heatwave, a coach party that booked by phone) throws the demand curve off. That’s exactly why it never publishes. It shows the forecast covers per day-part against what you’ve rostered, so you can see why it suggests pulling two from the 3–5pm lull before you agree. The manager who knows Saturday is the one deciding, not the model.

Partly, and it will tell you where it’s guessing. The demand forecast is only as good as the trading history it can read — if walk-ins and phone bookings never reach a system, the model under-forecasts your real peaks. Days running on POS-only data are badged differently from days enriched with bookings and weather, so you don’t trust a thin forecast like a rich one. Getting capture consistent is usually the first job, and it’s worth more than the roster tool.

No. It replaces the hour they spend rebuilding the grid from last year’s pattern, not the judgement about who works well together on a hard Saturday, who’s training up, and who asked for the early finish. It drafts and checks; the manager adjusts for what no data captures and signs off. Capacity comes back to the floor, not off the wage bill.

It should read recent trading — the demo forecasts off roughly the last four weeks of POS plus weather and known events, so seasonal shifts and a new nearby venue show up. Stale data is the main failure mode: a forecast built on figures from before you changed your hours or menu will quietly mis-staff every day. If your POS feed lags or only syncs weekly, fixing that cadence is part of the setup.

It runs on your own Power Platform tenancy against your POS and rostering systems — the data stays in your environment, not a third-party roster cloud. Staff ages and availability are read only to apply award rules and check who can work a shift; they don’t leave the workflow. We scope exactly which fields the agent reads and writes during setup, so there are no surprises. The demo runs entirely on fabricated data.

Treat the flags as a prompt to a person, not a ruling. The award check is deterministic — it applies the Restaurant Award MA000119 rules as configured (minimum engagement, junior shift limits, overtime thresholds, penalty-rate windows) and cites the clause — but awards get interpreted, enterprise agreements override them, and the rules change at the annual wage review. A flag means “look at this shift”; your payroll or HR person confirms it, and the accountability stays with a human.

What it takes to build

3–5 weeks · 4 phases

Reused from template~60%

Bespoke to this skin~40%

stack · Power Platform · POS + rostering API · award rule set · review UI

What it would cost

Fixed scope, fixed price, fixed dates.

Bite-sized first piece

One venue, one contained change

Pilot build

Most builds land here

Embedded support

Scale across venues on proof

Considering this for your venues?

The honest place to start is a bite-sized first piece — one venue, one contained change, low risk. Tell us where it hurts; we'll play it back, scope it, and show you what's possible.

Book a call How we work